‘Smart manufacturing’ involves the collection of data during the manufacturing process, with the aim of monitoring and optimising the manufacturing process. For example, data concerning the operation of machines on the factory floor, quality of the material and product, and environmental conditions is collected, processed, and analysed in order to determine whether the manufacturing process can be optimised in some way. As an example, following the analysis of data from a particular machine, it may become apparent that the machine should operate at a reduced speed in order to reduce the amount of down-time resulting from maintenance of the machine. As another example, the analysis of data collected from a particular machine may result in an indication that a change to the tooling of that machine would reduce variation in the physical dimensions of a part, thereby improving produce quality and reducing wastage.
Typically, data is collected by Internet of Things (IoT) devices. These devices include a sensor that collects data and a means of transferring the collected data (typically via a wireless communications link) to a computing system in which that data is processed.
In some existing smart manufacturing solutions, all data is analysed close to the point of collection (for example, on the factory floor). However, there are limitations on the amount of computing resources that can be deployed on the factory floor, and their ability to access any additional external data relevant to the process (e.g. offsite data stores, or data from other manufacturing facilities, or from the supply chain). The limitation on computing resources is further compounded by the environmental challenges that are particular to the factory floor (for example, a requirement for the computing resources to be sufficiently robust so as not to be damaged by dirt). These limitations on the amount of computing resources that can be deployed on the factory floor mean that the amount of data that is collected and analysed is limited. That is, only a relatively small window of data can be processed using computing resources deployed on the factory floor. Given the relatively small window of data, and its potential incompleteness, any temporal variations or noise in the manufacturing data can have a significant impact on the outcome of the data analysis.
In other existing solutions, data is collected and processed using cloud platforms (i.e. “in the cloud”), meaning that data is not processed on the factory floor. Analysing the data in the cloud allows an increased amount of data, and data from different locations and facilities, to be analysed (compared with data analysis on the factory floor). However, connectivity to the cloud is not guaranteed. For example, a natural disaster such as a large fire or earthquake may damage communications lines, disrupting the communications link between the factory and the cloud. This means that “mission critical” data processing (i.e. data that is required for the manufacturing process to run) may not be able to be processed, because of the disruption of the communications link. The consequence of the lack of processing of the mission critical data is that the factory may then be unable to run. In addition, there may be constraints related to latency, privacy and security that can limit the availability of data in the cloud.
Accordingly, there exists a need to improve the processing of data in smart manufacturing environments.